CN111314841A - WSN positioning method based on compressed sensing and improved genetic algorithm - Google Patents
WSN positioning method based on compressed sensing and improved genetic algorithm Download PDFInfo
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- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
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- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
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Abstract
A WSN positioning method based on compressed sensing and improved genetic algorithm relates to the field of WSN positioning and comprises the following steps: s1, initializing a network structure, uniformly distributing beacon nodes and randomly distributing unknown nodes. And S2, the nodes communicate with each other, and the beacon node receives the data packet transmitted by the unknown node. S3, primarily reducing the positioning area according to the communication result as a result of the first stage. And S4, dividing the network in the reduced interval and constructing a compressed sensing model. And S5, solving the compressed sensing model to further reduce the positioning area. And S6, implementing an improved genetic algorithm in the final region to carry out positioning accuracy optimization. And S7, outputting coordinates of all nodes to finish WSN positioning. The method reasonably applies the compressed sensing technology to reduce the positioning area, and uses the improved genetic algorithm to carry out precision optimization, thereby being a positioning method with low energy consumption and high precision.
Description
Technical Field
The invention relates to the field of positioning technology, in particular to a WSN positioning method.
Background
The node positioning technology is one of the supporting technologies of the wireless sensor network, and can be used for realizing a plurality of functions of target identification, monitoring, tracking and the like of the wireless sensor network. In recent years, positioning methods of wireless sensor networks have become mature, and particularly, stationary beacon type positioning methods have achieved high positioning accuracy, but most positioning technologies adopt relatively complex optimization algorithms, and require relatively high network energy consumption and hardware resources. Therefore, in recent years, researchers are always searching for a high-precision positioning algorithm suitable for the WSN with low energy consumption.
The current research focus of the positioning technology of the wireless sensor network is to reduce the network energy consumption and improve the positioning accuracy. The network energy consumption required by a general positioning algorithm mainly comes from node communication, and the mobile beacon type positioning algorithm also needs to add the energy consumption required by a mobile node, so that the algorithms proposed by a plurality of articles can use one-time communication, meanwhile, data packets of communication interaction are simple as much as possible, and then the precision problem caused by simple communication is considered. The accuracy problem of the positioning algorithm is mainly that a communication model is influenced by an actual environment, so that a measured distance error is caused, the error is delayed when the optimization algorithm is used for solving, and the error is amplified when the optimization algorithm is used for solving, so that the multi-stage positioning is popular, and the error diffusion can be restrained to a certain extent.
The positioning optimization algorithms are many, and the positioning problem is mostly reconstructed into an optimized extremum solving problem, so that the accuracy can be optimized by using a positioning technology such as a genetic algorithm, an ant colony algorithm, a particle swarm algorithm, a bats colony algorithm and the like. Certainly, many experts also perform detail adjustment aiming at the traditional DV-Hop and RSSI least square methods and the like, so that the positioning accuracy is improved. At present, however, the research on the positioning algorithm has a large development space, and many researchers hope to find a wireless sensor network positioning method with wide applicability, low energy consumption and ideal precision.
Disclosure of Invention
Aiming at the problems, the invention aims to realize a WSN positioning algorithm with low energy consumption and high precision, mainly aims to reduce the energy consumption required by network positioning, and secondly aims to improve the precision of the positioning algorithm.
The technical scheme provided by the invention is as follows:
a WSN positioning method based on compressed sensing and improved genetic algorithm comprises the following steps:
s1, initializing a network structure, uniformly distributing beacon nodes and randomly distributing unknown nodes;
s2, the nodes communicate with each other, and the beacon node receives a data packet transmitted by an unknown node;
s3, primarily reducing the positioning area according to the communication result as a result of the first stage.
S4, dividing the network in the reduced interval, and constructing a compressed sensing model;
s5, solving a compressed sensing model, and further reducing a positioning area;
s6, implementing an improved genetic algorithm in the final region to carry out positioning accuracy optimization;
and S7, outputting coordinates of all nodes to finish WSN positioning.
Further preferably, in the network distribution case in step S1, the beacon nodes are distributed at four vertices of the evenly divided rectangular grid, and the unknown nodes may be distributed at any position of the grid. The important information in the information report transmitted by the unknown node and the beacon node in step S2 is the number of the unknown node and the RSSI signal strength.
Further preferably, step S3 reduces the positioning area according to the geometric relationship between the communication result and the overlap, so that the required network power consumption is low. The inclusion boundary can be fault-tolerant, and the rectangle including the overlap region is selected as the final result after the overlap region is determined, and in order to increase the fault tolerance, it is necessary to extend one unit in each of the up, down, left and right directions.
Further preferably, step S4 and step S5 propose a compressed sensing model suitable for the positioning problem with low energy consumption, which can effectively further reduce the positioning area, and the meshing length can be changed according to the actual situation. The reduced perception model is only used as a boundary for solving the next stage of optimization, so that the method is simple and easy to implement.
Further preferably, step S6 proposes an improved genetic algorithm for localization accuracy optimization, and the localization model is changed to solve an optimization problem with constraints. The diffusivity of distance measurement errors is fully considered in the genetic algorithm, and a fitness function based on distance weighting is designed to reduce the errors.
In summary, the positioning method provided by the invention is a three-stage positioning method, the positioning area is reduced by utilizing compressed sensing, and the positioning accuracy is optimized by using a genetic algorithm, so that the positioning method is low in energy consumption and high in accuracy.
Drawings
The features and advantages of the present invention will be further explained with reference to the following detailed description of the embodiments of the present invention.
FIG. 1 is an overall flow chart of the present invention.
Fig. 2 is a detailed flow diagram of the stages of the present invention.
Fig. 3 is a schematic diagram of the packet contents of information transmission.
FIG. 4 is a schematic diagram of the first stage of the present invention for solving for overlapping regions.
Detailed Description
The patent provides a WSN positioning method based on compressed sensing and an improved genetic algorithm, which is suitable for small and medium-sized indoor sensor network structures. The method comprises the steps of adopting a three-stage positioning strategy, firstly reducing the range of nodes by utilizing node communication and geometric division, then reconstructing a positioning model in a reduced interval by utilizing a compressed sensing method, enabling the positioning model to correspond to members in the compressed sensing model, reducing energy consumption required by positioning by utilizing the sparse characteristic in the compressed sensing technology, and realizing rough positioning; and finally, designing an optimized genetic algorithm to improve the precision by using the positioning result of the second stage as a boundary, and realizing the accurate positioning of the final target node.
The WSN positioning method based on compressed sensing and the improved genetic algorithm can be basically achieved, the main core content is that the compressed sensing model is used for solving and achieving low-energy-consumption rough positioning, and then the improved genetic algorithm is used for optimizing positioning accuracy. The following describes in detail the implementation of the overall process in flow chart 1.
N unknown nodes to be positioned in the whole network are assumed to be numbered 1: N respectively and are randomly distributed in each corner of an area to be positioned. The number of the beacon nodes which can be provided at present is L, the whole area to be positioned can be restricted to a rectangular area, and the L beacon nodes are deployed at four vertexes of the rectangle.
First, all nodes will perform relatively light network communication, and the format of the transmitted data packet is as follows in fig. 2. Energy signals RSSI are sent among all nodes, and the relationship between energy and distance is as follows:
wherein, P0Is the initial energy of transmission, P (d)0) Is energy at d0Energy loss in units, η is the energy loss coefficient, d is the distance, xσIs an error constant that satisfies the gaussian distribution. Therefore, after a period of communication, the beacon node can quickly determine which unknown nodes can be served by the beacon node, and can calculate the distance from the unknown nodes for solving the position of the nodes.
The main purpose of the first stage is to reduce the positioning area and improve the solution search efficiency for the following two stages. In fig. 3, a, B, and C are beacons, and a sequence of beacons belonging to an unknown node U may be a possible area of the unknown node through an overlapping portion. In consideration of measurement errors, a rectangle which can maximally contain an overlapping portion is taken, and a unit is extended in the x and y directions to serve as a boundary guarantee, and a possible area is finally determined. In the first stage, the possible area needs to be divided according to a certain step length d to generate M grids.
Now that the possible area where the unknown node is located is obtained, the divided grids are numbered 1: M at this stage, and then the unknown node may belong to one of the grids (if it can be considered to belong to any grid with a boundary in common at the boundary). Defining an M-dimensional vector S, if the unknown node U belongs to the kth grid, Sk1, otherwise Sk0. The vector S is a high-dimensional sparse vector and can be used as a sparse vector for compressed sensing; then the distance d of the unknown node from the four beacon nodes of the matrixi,jForming a sampling vector Y, i.e. Yi＝dij(ii) a Finally, the compressed sensing observation matrix defines a matrix Φ (M × N), where M is 4 to conform to the model presented herein, and the matrix represents the distances from the four beacon nodes providing reference to the center of the N grids. Then the whole compressed sensing model can be constructed as follows:
Y＝ΦS
in fact, the positioning process is simplified into the reconstruction process of the sparse signal S, and because of the sparse property, the problem is actually an underdetermined equation system solving problem.
Suppose that the four beacons on which the original unknown node depends are (x)i,yi) 1,2,3,4, four vertex coordinates (l) of the restricted areai,hi) i-1, 2,3,4, we will implement a constrained optimization problem solution in this localized region. First, a solution model is constructed, assuming that the coordinates of the unknown node U are (UX, UY). The distances from the four beacon nodes to the U are diuThen the measurement error is taken as the objective function:
obviously, considering the degree of dependence of the beacon nodes, the measurement error and the like, if the distance d is measureduiFurther away, it is clear that the lower the level of trust that this node is trusted, the lower its impact can be. Therefore, the weight p needs to be introducediIndicating the ith beacon dependency.
Finally, the solved model is:
the constraint is l1≤UX≤l2，h1≤UY≤h3. Next, solving the optimized extremum problem with constraint conditions, which we use optimized genetic algorithm to solve, the solving steps are as follows:
step1 initial population solutions, randomly producing k solutions in the region.
And Step2, starting iteration, calculating fitness function values corresponding to the k solutions, and sequencing according to the fitness function values.
And Step3, selecting and operating, reserving the optimal solution in the previous Step, then performing cross operation on the rest solutions pairwise, eliminating the generated offspring once the offspring does not meet the population constraint condition in the cross process, and randomly generating a new solution for supplement to ensure the whole population quantity. Except for cross operation, part of individuals are selected with a certain probability to carry out disturbance variation, the adopted mode is coordinate fine jitter, and if constraint conditions are damaged, the same method is adopted, and new solutions are eliminated and replaced.
And Step4, solving the fitness function values of the new solution obtained in Step3, sorting the fitness function values according to the fitness function values, screening out a globally optimal solution, if the error of the globally optimal solution is extremely small or the iteration number reaches the upper limit, ending the iteration, and if not, returning to Step3 to continue the iteration.
The present invention has been described in detail with respect to the case of each flow implementation scenario, and those skilled in the art will appreciate that appropriate variations and modifications may be made without departing from the spirit and scope thereof.
Claims (6)
1. A WSN positioning method based on compressed sensing and improved genetic algorithm is characterized by comprising the following steps:
s1, initializing a network structure, uniformly distributing beacon nodes and randomly distributing unknown nodes;
s2, the nodes communicate with each other, and the beacon node receives a data packet transmitted by an unknown node;
s3, preliminarily reducing a positioning area according to a communication result as a result of a first stage;
s4, dividing the network in the reduced interval, and constructing a compressed sensing model;
s5, solving a compressed sensing model, and further reducing a positioning area;
s6, implementing an improved genetic algorithm in the final region to carry out positioning accuracy optimization;
and S7, outputting coordinates of all nodes to finish WSN positioning.
2. The WSN localization method based on compressed sensing and improved genetic algorithm according to claim 1, comprising the steps of:
step one, constructing a basic network topology structure, uniformly dividing the whole rectangular region to be positioned by taking the communication radius of a beacon node as a reference, placing L beacon nodes at four vertex angles of each rectangle, and properly selecting a communication radius R according to the coverage rate and the communication energy consumption;
secondly, in the first stage, respectively numbering 1: N unknown nodes to be positioned, then carrying out communication between the unknown nodes and beacon nodes based on an RSSI (received signal strength indicator) information propagation mode, putting the numbers of the unknown nodes in data packets to be transmitted to the beacon nodes by the unknown nodes in the communication process, and then reducing the positioning areas of the unknown nodes into a rectangular area by combining the network distribution condition according to the relationship between the beacon nodes and the unknown nodes;
step three, performing a coarse positioning process of a second stage, constructing a model for solving the position of an unknown node by using a compressed sensing technology, and performing grid division on the positioning area obtained in the step two to obtain M rectangular grids which are respectively numbered 1: M;
selecting four beacon nodes of a rectangle where the unknown nodes are located, and taking a distance matrix between the beacon nodes and the grid center as an observation matrix of the compressed sensing model and recording the distance matrix as phi; taking the distance between the beacon node and the unknown node as a sampling matrix, and recording as Y; solving a sparse signal vector V as the rough location of an unknown node according to a compressed sensing model, and indicating whether a certain node belongs to a certain grid area;
step five, the third stage realizes accurate positioning, obtains the grid area where the unknown node is located according to the result of the step four, and positions the area at the boundary of the distribution of the unknown node; and finally, converting the positioning problem into an optimization problem with constraint conditions, and performing iterative optimization by using a genetic algorithm to determine the accurate coordinates of all unknown nodes.
3. The WSN localization method based on compressed sensing and improved genetic algorithm according to claim 2, wherein the second step comprises:
energy signals RSSI are sent among all nodes, and the relationship between energy and distance is as follows:
wherein，P0Is the initial energy of transmission, P (d)0) Is energy at d0Energy loss in units, η is the energy loss coefficient, d is the distance, xσIs an error constant that satisfies a gaussian distribution; after a period of communication, the L beacon nodes can determine which unknown nodes can be served by the beacon nodes, and calculate the distance from the unknown nodes for solving the node position.
4. The WSN localization method based on compressed sensing and improved genetic algorithm according to claim 3, wherein the third step comprises:
defining an M-dimensional vector S for each unknown node, if the unknown node U belongs to the kth grid of the M grids, Sk1, otherwise Sk0; the vector S is a compressed sensing sparse vector, and the distances d between the unknown node and four beacon nodes of the matrixi,jForming a sampling vector Y, i.e. Yi＝dijThe calculation is carried out by the formula (1); defining a matrix Φ (M × N), let M equal to 4, the matrix means the distances from the four beacons providing reference to the N grid centers, and then the whole compressed sensing model is:
Y＝ΦS (2)。
5. the WSN localization method based on compressed sensing and improved genetic algorithm according to claim 2, wherein the fifth step comprises:
the area of the unknown node is limited in the second stage result, and the four beacons on which the original unknown node depends are assumed to be (x)i,yi) 1,2,3,4, four vertex coordinates (l) of the restricted areai,hi) First, a solution model is constructed, assuming that the coordinates of an unknown node U are (UX, UY) and the distances from four beacon nodes to U are diuThen the measurement error is taken as the objective function:
introducing weights piTo representThe ith beacon dependency is a function of the beacon dependency,
finally, the solved model is:
the constraint is l1≤UX≤l2，h1≤UY≤h3。
6. The WSN localization method based on compressed sensing and improved genetic algorithm according to claim 2, wherein the genetic algorithm comprises the steps of:
step1, initial population solution, randomly generating k solutions in the region;
step2, starting iteration, calculating fitness function values corresponding to the k solutions, and sequencing according to the fitness function values;
step3, selecting operation, reserving the optimal solution in the previous Step, and then performing cross operation on the rest solutions in pairs to ensure the number of the whole population; meanwhile, selecting part of individuals with a certain probability for disturbance variation, adopting a mode of coordinate fine jitter, and eliminating and replacing new solutions if constraint conditions are destroyed by adopting the same method;
and Step4, solving the fitness function values of the new solution obtained in Step3, sorting the fitness function values according to the fitness function values, screening out a globally optimal solution, if the error of the globally optimal solution is extremely small or the iteration number reaches the upper limit, ending the iteration, and if not, returning to Step3 to continue the iteration.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111800825A (en) * | 2020-07-20 | 2020-10-20 | 中南大学 | Dynamic retransmission method of data in wireless sensor network based on compressed sensing |
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